Adapting the Segment Anything Model for Volumetric X-ray Data-Sets of Arbitrary Sizes
Gruber R, Rüger S, Wittenberg T (2024)
Publication Type: Journal article
Publication year: 2024
Journal
Book Volume: 14
Article Number: 3391
Journal Issue: 8
DOI: 10.3390/app14083391
Abstract
We propose a new approach for volumetric instance segmentation in X-ray Computed Tomography (CT) data for Non-Destructive Testing (NDT) by combining the Segment Anything Model (SAM) with tile-based Flood Filling Networks (FFN). Our work evaluates the performance of SAM on volumetric NDT data-sets and demonstrates its effectiveness to segment instances in challenging imaging scenarios. We implemented and evaluated techniques to extend the image-based SAM algorithm for the use with volumetric data-sets, enabling the segmentation of three-dimensional objects using FFN’s spatial adaptability. The tile-based approach for SAM leverages FFN’s capabilities to segment objects of any size. We also explore the use of dense prompts to guide SAM in combining segmented tiles for improved segmentation accuracy. Our research indicates the potential of combining SAM with FFN for volumetric instance segmentation tasks, particularly in NDT scenarios and segmenting large entities and objects. While acknowledging remaining limitations, our study provides insights and establishes a foundation for advancements in instance segmentation in NDT scenarios.
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Germany (DE)How to cite
APA:
Gruber, R., Rüger, S., & Wittenberg, T. (2024). Adapting the Segment Anything Model for Volumetric X-ray Data-Sets of Arbitrary Sizes. Applied Sciences, 14(8). https://doi.org/10.3390/app14083391
MLA:
Gruber, Roland, Steffen Rüger, and Thomas Wittenberg. "Adapting the Segment Anything Model for Volumetric X-ray Data-Sets of Arbitrary Sizes." Applied Sciences 14.8 (2024).
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